In this context, we introduce our library for differentiable robot models which implements analytical differentiable and learnable robot models in PyTorch [1]}. In contrast to existing libraries, our library differs in two ways from existing libraries: 1) our library focuses on differentiable robot models (instead of full simulations with environment modeling), and provides differentiable kinematic and dynamic representations. These robot models can then, for instance, be used for control of the modeled robots in either simulation or on hardware [2]}, [3]}. 2) Our library provides differentiable analytical rigid body simulations that can selectively make rigid bodies learnable. When a rigid body becomes learnable, we can either identify its physical constants (such as mass) from data, or learn flexible representations (such as neural networks) for the parameters of the rigid body from data. Our learnable robot models are implemented as general torch modules, and thus can be easily used within typical deep learning pipelines.
